This is our attempt to solve real-time multi-label classification challenge organized on AiChallenger by MeiTu company.
Simple baseline is composed of the following steps:
- Extract the first frame from each video.
python scripts/video_to_frames.py --ann_file /mnt/ssd1/dataset/short_video_trainingset_annotations.txt.082902 --data_dir /mnt/ssd1/dataset/train/ --out_dir /mnt/ssd1/dataset/train_jpg
python scripts/video_to_frames.py --ann_file /mnt/ssd1/dataset/short_video_validationset_annotations.txt.0829 --data_dir /mnt/ssd1/dataset/val/ --out_dir /mnt/ssd1/dataset/val_jpg
- Extract features
CUDA_VISIBLE_DEVICES=0 screen python extract_features.py --ann_file /mnt/ssd1/dataset/new_short_video_validationset_annotations.txt.txt --data_dir /mnt/ssd1/dataset/val_jpg/ --out_dir /mnt/ssd1/dataset/val_features
CUDA_VISIBLE_DEVICES=0 screen python extract_features.py --ann_file /mnt/ssd1/dataset/new_short_video_trainingset_annotations.txt.txt --data_dir /mnt/ssd1/dataset/train_jpg/ --out_dir /mnt/ssd1/dataset/train_features
- Employ lightgbm to classify extracted features
python lightgbm_example.py --train_dir /mnt/ssd1/dataset/train_features/ --val_dir /mnt/ssd1/dataset/val_features/
python train_net.py --train_ann_file /mnt/ssd1/dataset/new_short_video_trainingset_annotations.txt.txt --train_data_dir /mnt/ssd1/dataset/train_jpg/ --val_ann_fil
e /mnt/ssd1/dataset/new_short_video_validationset_annotations.txt.txt --val_data_dir /mnt/ssd1/dataset/val_jpg/ -a se_resnet50
The better way
screen ./scripts/unstable/train_baseline.sh 4 se_resnet50 0.0001 128
CUDA_VISIBLE_DEVICES=2 python eval_net.py -b 32 -a se_resnet50 -w /mnt/ssd1/easygold/challengerai-mlsv2018/logs/un_baseline_2018-10-10_16-57-05/checkpoint.pth.tar --ann_fi
le /mnt/ssd1/dataset/short_video_validationset_annotations.txt --data_dir /mnt/ssd1/dataset/val/ -t 1
Look for the frequence of tags to determine what tags often meet together. May be useful for multilabel analysis.
python tags_analysis.py --ann_file short_video_trainingnset_annotations.txt --out_dir mnt/ssd1/dataser/train_tags_analysis
Run meta classifier. It is based on majority voting. You can include uo to 5 single model predictions(lightgbm, catboost, log regression, neural network from pickles. Time for one prediction is printed for every model.
CUDA_VISIBLE_DEVICES=2 python infer_meta_classifier.py --train_dir /mnt/ssd1/dataset/multi_class/6_frames_train_se_resnet_merged/ --val_dir /mnt/ssd1/dataset/multi_class/6_frames_val_se_resnet_merged/ -multi_label -val_1_frame -net_pred -lgb -logreg -svm -catboost -voting
Files required for building docker are stored here.
Following packages are required to run the code:
- PyTorch
- PyAV
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent